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( Nano Banana 2
), are available to use with Firebase AI Logic! Learn more.
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. Learn more.
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Generate text using the Gemini API Stay organized with collections
Save and categorize content based on your preferences.
You can ask a Gemini
model to generate text from a text-only prompt or
a multimodal prompt. When you use Firebase AI Logic
, you can make this
request directly from your app.
Multimodal prompts can include multiple types of input
(like text along with images, PDFs, plain-text files, audio, and video).
This guide shows how to generate text from a text-only prompt and from a basic
multimodal prompt that includes a file.
arrow_downward
Jump to code for text-only input
arrow_downward
Jump to code for multimodal input
arrow_downward
Jump to code for streamed responses
Before you begin
Click your Gemini API
provider to view provider-specific content
and code on this page.
Gemini Developer API
Vertex AI Gemini API
If you haven't already, complete the getting started guide
, which describes how to
set up your Firebase project, connect your app to Firebase, add the SDK,
initialize the backend service for your chosen Gemini API
provider, and
create a GenerativeModel
instance.
All our docs assume that you're using the latest versions
of the Firebase AI Logic
SDKs.
For testing and iterating on your prompts, we recommend using Google AI Studio
. Generate text from text-only input
Before trying this sample, complete the Before you begin
section of this guide
to set up your project and app. In that section, you'll also click a button for your chosen Gemini API
provider so that you see provider-specific content
on this page .
You can ask a Gemini
model to generate text by prompting with text-only
input.
Swift
You can call generateContent()
to generate text from text-only input.
import
FirebaseAILogic
// Initialize the Gemini Developer API backend service
let
ai
=
FirebaseAI
.
firebaseAI
(
backend
:
.
googleAI
())
// Create a `GenerativeModel` instance with a model that supports your use case
let
model
=
ai
.
generativeModel
(
modelName
:
"gemini-3.5-flash"
)
// Provide a prompt that contains text
let
prompt
=
"Write a story about a magic backpack."
// To generate text output, call generateContent with the text input
let
response
=
try
await
model
.
generateContent
(
prompt
)
print
(
response
.
text
??
"No text in response."
)
Kotlin
You can call generateContent()
to generate text from text-only input.
For Kotlin, the methods in this SDK are suspend functions and need to be called
from a Coroutine scope
.
// Initialize the Gemini Developer API backend service
// Create a `GenerativeModel` instance with a model that supports your use case
val
model
=
Firebase
.
ai
(
backend
=
GenerativeBackend
.
googleAI
())
.
generativeModel
(
"gemini-3.5-flash"
)
// Provide a prompt that contains text
val
prompt
=
"Write a story about a magic backpack."
// To generate text output, call generateContent with the text input
val
response
=
model
.
generateContent
(
prompt
)
print
(
response
.
text
)
Java
You can call generateContent()
to generate text from text-only input.
For Java, the methods in this SDK return a ListenableFuture
.
// Initialize the Gemini Developer API backend service
// Create a `GenerativeModel` instance with a model that supports your use case
GenerativeModel
ai
=
FirebaseAI
.
getInstance
(
GenerativeBackend
.
googleAI
())
.
generativeModel
(
"gemini-3.5-flash"
);
// Use the GenerativeModelFutures Java compatibility layer which offers
// support for ListenableFuture and Publisher APIs
GenerativeModelFutures
model
=
GenerativeModelFutures
.
from
(
ai
);
// Provide a prompt that contains text
Content
prompt
=
new
Content
.
Builder
()
.
addText
(
"Write a story about a magic backpack."
)
.
build
();
// To generate text output, call generateContent with the text input
ListenableFuture<GenerateContentResponse>
response
=
model
.
generateContent
(
prompt
);
Futures
.
addCallback
(
response
,
new
FutureCallback<GenerateContentResponse>
()
{
@Override
public
void
onSuccess
(
GenerateContentResponse
result
)
{
String
resultText
=
result
.
getText
();
System
.
out
.
println
(
resultText
);
}
@Override
public
void
onFailure
(
Throwable
t
)
{
t
.
printStackTrace
();
}
},
executor
);
Web
You can call generateContent()
to generate text from text-only input.
import
{
initializeApp
}
from
"firebase/app"
;
import
{
getAI
,
getGenerativeModel
,
GoogleAIBackend
}
from
"firebase/ai"
;
// TODO(developer) Replace the following with your app's Firebase configuration
// See: https://firebase.google.com/docs/web/learn-more#config-object
const
firebaseConfig
=
{
// ...
};
// Initialize FirebaseApp
const
firebaseApp
=
initializeApp
(
firebaseConfig
);
// Initialize the Gemini Developer API backend service
const
ai
=
getAI
(
firebaseApp
,
{
backend
:
new
GoogleAIBackend
()
});
// Create a `GenerativeModel` instance with a model that supports your use case
const
model
=
getGenerativeModel
(
ai
,
{
model
:
"gemini-3.5-flash"
});
// Wrap in an async function so you can use await
async
function
run
()
{
// Provide a prompt that contains text
const
prompt
=
"Write a story about a magic backpack."
// To generate text output, call generateContent with the text input
const
result
=
await
model
.
generateContent
(
prompt
);
const
response
=
result
.
response
;
const
text
=
response
.
text
();
console
.
log
(
text
);
}
run
();
Dart
You can call generateContent()
to generate text from text-only input.
import
'package:firebase_ai/firebase_ai.dart'
;
import
'package:firebase_core/firebase_core.dart'
;
import
'firebase_options.dart'
;
// Initialize FirebaseApp
await
Firebase
.
initializeApp
(
options:
DefaultFirebaseOptions
.
currentPlatform
,
);
// Initialize the Gemini Developer API backend service
// Create a `GenerativeModel` instance with a model that supports your use case
final
model
=
FirebaseAI
.
googleAI
().
generativeModel
(
model:
'gemini-3.5-flash'
);
// Provide a prompt that contains text
final
prompt
=
[
Content
.
text
(
'Write a story about a magic backpack.'
)];
// To generate text output, call generateContent with the text input
final
response
=
await
model
.
generateContent
(
prompt
);
print
(
response
.
text
);
Unity
You can call GenerateContentAsync()
to generate text from text-only input.
using
Firebase
;
using
Firebase.AI
;
// Initialize the Gemini Developer API backend service
var
ai
=
FirebaseAI
.
GetInstance
(
FirebaseAI
.
Backend
.
GoogleAI
());
// Create a `GenerativeModel` instance with a model that supports your use case
var
model
=
ai
.
GetGenerativeModel
(
modelName
:
"gemini-3.5-flash"
);
// Provide a prompt that contains text
var
prompt
=
"Write a story about a magic backpack."
;
// To generate text output, call GenerateContentAsync with the text input
var
response
=
await
model
.
GenerateContentAsync
(
prompt
);
UnityEngine
.
Debug
.
Log
(
response
.
Text
??
"No text in response."
);
Learn how to choose a model
appropriate for your use case and app.
Generate text from text-and-file (multimodal) input
Before trying this sample, complete the Before you begin
section of this guide
to set up your project and app. In that section, you'll also click a button for your chosen Gemini API
provider so that you see provider-specific content
on this page .
You can ask a Gemini
model to
generate text by prompting with text and a file—providing each
input file's mimeType
and the file itself. Find requirements and recommendations for input files
later on this page.
The following example shows the basics of how to generate text from a file input
by analyzing a single video file provided as inline data (base64-encoded file).
Note that this example shows providing the file inline, but the SDKs also
support providing a YouTube URL
.
Need a sample video file?
You can use this publicly available file with a MIME type of video/mp4
( view or download file
). https://storage.googleapis.com/cloud-samples-data/video/animals.mp4
Swift
You can call generateContent()
to generate text from multimodal input of text and video files.
import
FirebaseAILogic
// Initialize the Gemini Developer API backend service
let
ai
=
FirebaseAI
.
firebaseAI
(
backend
:
.
googleAI
())
// Create a `GenerativeModel` instance with a model that supports your use case
let
model
=
ai
.
generativeModel
(
modelName
:
"gemini-3.5-flash"
)
// Provide the video as `Data` with the appropriate MIME type.
let
video
=
InlineDataPart
(
data
:
try
Data
(
contentsOf
:
videoURL
),
mimeType
:
"video/mp4"
)
// Provide a text prompt to include with the video
let
prompt
=
"What is in the video?"
// To generate text output, call generateContent with the text and video
let
response
=
try
await
model
.
generateContent
(
video
,
prompt
)
print
(
response
.
text
??
"No text in response."
)
Kotlin
You can call generateContent()
to generate text from multimodal input of text and video files.
For Kotlin, the methods in this SDK are suspend functions and need to be called
from a Coroutine scope
.
// Initialize the Gemini Developer API backend service
// Create a `GenerativeModel` instance with a model that supports your use case
val
model
=
Firebase
.
ai
(
backend
=
GenerativeBackend
.
googleAI
())
.
generativeModel
(
"gemini-3.5-flash"
)
val
contentResolver
=
applicationContext
.
contentResolver
contentResolver
.
openInputStream
(
videoUri
).
use
{
stream
->
stream
?.
let
{
val
bytes
=
stream
.
readBytes
()
// Provide a prompt that includes the video specified above and text
val
prompt
=
content
{
inlineData
(
bytes
,
"video/mp4"
)
text
(
"What is in the video?"
)
}
// To generate text output, call generateContent with the prompt
val
response
=
model
.
generateContent
(
prompt
)
Log
.
d
(
TAG
,
response
.
text
?:
""
)
}
}
Java
You can call generateContent()
to generate text from multimodal input of text and video files.
For Java, the methods in this SDK return a ListenableFuture
.
// Initialize the Gemini Developer API backend service
// Create a `GenerativeModel` instance with a model that supports your use case
GenerativeModel
ai
=
FirebaseAI
.
getInstance
(
GenerativeBackend
.
googleAI
())
.
generativeModel
(
"gemini-3.5-flash"
);
// Use the GenerativeModelFutures Java compatibility layer which offers
// support for ListenableFuture and Publisher APIs
GenerativeModelFutures
model
=
GenerativeModelFutures
.
from
(
ai
);
ContentResolver
resolver
=
getApplicationContext
().
getContentResolver
();
try
(
InputStream
stream
=
resolver
.
openInputStream
(
videoUri
))
{
File
videoFile
=
new
File
(
new
URI
(
videoUri
.
toString
()));
int
videoSize
=
(
int
)
videoFile
.
length
();
byte
[]
videoBytes
=
new
byte
[
videoSize
]
;
if
(
stream
!=
null
)
{
stream
.
read
(
videoBytes
,
0
,
videoBytes
.
length
);
stream
.
close
();
// Provide a prompt that includes the video specified above and text
Content
prompt
=
new
Content
.
Builder
()
.
addInlineData
(
videoBytes
,
"video/mp4"
)
.
addText
(
"What is in the video?"
)
.
build
();
// To generate text output, call generateContent with the prompt
ListenableFuture<GenerateContentResponse>
response
=
model
.
generateContent
(
prompt
);
Futures
.
addCallback
(
response
,
new
FutureCallback<GenerateContentResponse>
()
{
@Override
public
void
onSuccess
(
GenerateContentResponse
result
)
{
String
resultText
=
result
.
getText
();
System
.
out
.
println
(
resultText
);
}
@Override
public
void
onFailure
(
Throwable
t
)
{
t
.
printStackTrace
();
}
},
executor
);
}
}
catch
(
IOException
e
)
{
e
.
printStackTrace
();
}
catch
(
URISyntaxException
e
)
{
e
.
printStackTrace
();
}
Web
You can call generateContent()
to generate text from multimodal input of text and video files.
import
{
initializeApp
}
from
"firebase/app"
;
import
{
getAI
,
getGenerativeModel
,
GoogleAIBackend
}
from
"firebase/ai"
;
// TODO(developer) Replace the following with your app's Firebase configuration
// See: https://firebase.google.com/docs/web/learn-more#config-object
const
firebaseConfig
=
{
// ...
};
// Initialize FirebaseApp
const
firebaseApp
=
initializeApp
(
firebaseConfig
);
// Initialize the Gemini Developer API backend service
const
ai
=
getAI
(
firebaseApp
,
{
backend
:
new
GoogleAIBackend
()
});
// Create a `GenerativeModel` instance with a model that supports your use case
const
model
=
getGenerativeModel
(
ai
,
{
model
:
"gemini-3.5-flash"
});
// Converts a File object to a Part object.
async
function
fileToGenerativePart
(
file
)
{
const
base64EncodedDataPromise
=
new
Promise
((
resolve
)
=>
{
const
reader
=
new
FileReader
();
reader
.
onloadend
=
()
=>
resolve
(
reader
.
result
.
split
(
','
)[
1
]);
reader
.
readAsDataURL
(
file
);
});
return
{
inlineData
:
{
data
:
await
base64EncodedDataPromise
,
mimeType
:
file
.
type
},
};
}
async
function
run
()
{
// Provide a text prompt to include with the video
const
prompt
=
"What do you see?"
;
const
fileInputEl
=
document
.
querySelector
(
"input[type=file]"
);
const
videoPart
=
await
fileToGenerativePart
(
fileInputEl
.
files
[
0
]);
// To generate text output, call generateContent with the text and video
const
result
=
await
model
.
generateContent
([
prompt
,
videoPart
]);
const
response
=
result
.
response
;
const
text
=
response
.
text
();
console
.
log
(
text
);
}
run
();
Dart
You can call generateContent()
to generate text from multimodal input of text and video files.
import
'package:firebase_ai/firebase_ai.dart'
;
import
'package:firebase_core/firebase_core.dart'
;
import
'firebase_options.dart'
;
// Initialize FirebaseApp
await
Firebase
.
initializeApp
(
options:
DefaultFirebaseOptions
.
currentPlatform
,
);
// Initialize the Gemini Developer API backend service
// Create a `GenerativeModel` instance with a model that supports your use case
final
model
=
FirebaseAI
.
googleAI
().
generativeModel
(
model:
'gemini-3.5-flash'
);
// Provide a text prompt to include with the video
final
prompt
=
TextPart
(
"What's in the video?"
);
// Prepare video for input
final
video
=
await
File
(
'video0.mp4'
).
readAsBytes
();
// Provide the video as `Data` with the appropriate mimetype
final
videoPart
=
InlineDataPart
(
'video/mp4'
,
video
);
// To generate text output, call generateContent with the text and images
final
response
=
await
model
.
generateContent
([
Content
.
multi
([
prompt
,
...
videoPart
])
]);
print
(
response
.
text
);
Unity
You can call GenerateContentAsync()
to generate text from multimodal input of text and video files.
using
Firebase
;
using
Firebase.AI
;
// Initialize the Gemini Developer API backend service
var
ai
=
FirebaseAI
.
GetInstance
(
FirebaseAI
.
Backend
.
GoogleAI
());
// Create a `GenerativeModel` instance with a model that supports your use case
var
model
=
ai
.
GetGenerativeModel
(
modelName
:
"gemini-3.5-flash"
);
// Provide the video as `data` with the appropriate MIME type.
var
video
=
ModelContent
.
InlineData
(
"video/mp4"
,
System
.
IO
.
File
.
ReadAllBytes
(
System
.
IO
.
Path
.
Combine
(
UnityEngine
.
Application
.
streamingAssetsPath
,
"yourVideo.mp4"
)));
// Provide a text prompt to include with the video
var
prompt
=
ModelContent
.
Text
(
"What is in the video?"
);
// To generate text output, call GenerateContentAsync with the text and video
var
response
=
await
model
.
GenerateContentAsync
(
new
[]
{
video
,
prompt
});
UnityEngine
.
Debug
.
Log
(
response
.
Text
??
"No text in response."
);
Learn how to choose a model
appropriate for your use case and app.
Stream the response
Before trying this sample, complete the Before you begin
section of this guide
to set up your project and app. In that section, you'll also click a button for your chosen Gemini API
provider so that you see provider-specific content
on this page .
You can achieve faster interactions by not waiting for the entire result from
the model generation, and instead use streaming to handle partial results.
To stream the response, call generateContentStream
.
View example: Stream generated text from text-only input
Swift
You can call generateContentStream()
to stream generated text from text-only input.
import
FirebaseAILogic
// Initialize the Gemini Developer API backend service
let
ai
=
FirebaseAI
.
firebaseAI
(
backend
:
.
googleAI
())
// Create a `GenerativeModel` instance with a model that supports your use case
let
model
=
ai
.
generativeModel
(
modelName
:
"gemini-3.5-flash"
)
// Provide a prompt that contains text
let
prompt
=
"Write a story about a magic backpack."
// To stream generated text output, call generateContentStream with the text input
let
contentStream
=
try
model
.
generateContentStream
(
prompt
)
for
try
await
chunk
in
contentStream
{
if
let
text
=
chunk
.
text
{
print
(
text
)
}
}
Kotlin
You can call generateContentStream()
to stream generated text from text-only input.
For Kotlin, the methods in this SDK are suspend functions and need to be called
from a Coroutine scope
.
// Initialize the Gemini Developer API backend service
// Create a `GenerativeModel` instance with a model that supports your use case
val
model
=
Firebase
.
ai
(
backend
=
GenerativeBackend
.
googleAI
())
.
generativeModel
(
"gemini-3.5-flash"
)
// Provide a prompt that includes only text
val
prompt
=
"Write a story about a magic backpack."
// To stream generated text output, call generateContentStream and pass in the prompt
var
response
=
""
model
.
generateContentStream
(
prompt
).
collect
{
chunk
->
print
(
chunk
.
text
)
response
+=
chunk
.
text
}
Java
You can call generateContentStream()
to stream generated text from text-only input.
For Java, the streaming methods in this SDK return a Publisher
type from the Reactive Streams library
.
// Initialize the Gemini Developer API backend service
// Create a `GenerativeModel` instance with a model that supports your use case
GenerativeModel
ai
=
FirebaseAI
.
getInstance
(
GenerativeBackend
.
googleAI
())
.
generativeModel
(
"gemini-3.5-flash"
);
// Use the GenerativeModelFutures Java compatibility layer which offers
// support for ListenableFuture and Publisher APIs
GenerativeModelFutures
model
=
GenerativeModelFutures
.
from
(
ai
);
// Provide a prompt that contains text
Content
prompt
=
new
Content
.
Builder
()
.
addText
(
"Write a story about a magic backpack."
)
.
build
();
// To stream generated text output, call generateContentStream with the text input
Publisher<GenerateContentResponse>
streamingResponse
=
model
.
generateContentStream
(
prompt
);
// Subscribe to partial results from the response
final
String
[]
fullResponse
=
{
""
};
streamingResponse
.
subscribe
(
new
Subscriber<GenerateContentResponse>
()
{
@Override
public
void
onNext
(
GenerateContentResponse
generateContentResponse
)
{
String
chunk
=
generateContentResponse
.
getText
();
fullResponse
[
0
]
+=
chunk
;
}
@Override
public
void
onComplete
()
{
System
.
out
.
println
(
fullResponse
[
0
]
);
}
@Override
public
void
onError
(
Throwable
t
)
{
t
.
printStackTrace
();
}
@Override
public
void
onSubscribe
(
Subscription
s
)
{
}
});
Web
You can call generateContentStream()
to stream generated text from text-only input.
import
{
initializeApp
}
from
"firebase/app"
;
import
{
getAI
,
getGenerativeModel
,
GoogleAIBackend
}
from
"firebase/ai"
;
// TODO(developer) Replace the following with your app's Firebase configuration
// See: https://firebase.google.com/docs/web/learn-more#config-object
const
firebaseConfig
=
{
// ...
};
// Initialize FirebaseApp
const
firebaseApp
=
initializeApp
(
firebaseConfig
);
// Initialize the Gemini Developer API backend service
const
ai
=
getAI
(
firebaseApp
,
{
backend
:
new
GoogleAIBackend
()
});
// Create a `GenerativeModel` instance with a model that supports your use case
const
model
=
getGenerativeModel
(
ai
,
{
model
:
"gemini-3.5-flash"
});
// Wrap in an async function so you can use await
async
function
run
()
{
// Provide a prompt that contains text
const
prompt
=
"Write a story about a magic backpack."
// To stream generated text output, call generateContentStream with the text input
const
result
=
await
model
.
generateContentStream
(
prompt
);
for
await
(
const
chunk
of
result
.
stream
)
{
const
chunkText
=
chunk
.
text
();
console
.
log
(
chunkText
);
}
console
.
log
(
'aggregated response: '
,
await
result
.
response
);
}
run
();
Dart
You can call generateContentStream()
to stream generated text from text-only input.
import
'package:firebase_ai/firebase_ai.dart'
;
import
'package:firebase_core/firebase_core.dart'
;
import
'firebase_options.dart'
;
// Initialize FirebaseApp
await
Firebase
.
initializeApp
(
options:
DefaultFirebaseOptions
.
currentPlatform
,
);
// Initialize the Gemini Developer API backend service
// Create a `GenerativeModel` instance with a model that supports your use case
final
model
=
FirebaseAI
.
googleAI
().
generativeModel
(
model:
'gemini-3.5-flash'
);
// Provide a prompt that contains text
final
prompt
=
[
Content
.
text
(
'Write a story about a magic backpack.'
)];
// To stream generated text output, call generateContentStream with the text input
final
response
=
model
.
generateContentStream
(
prompt
);
await
for
(
final
chunk
in
response
)
{
print
(
chunk
.
text
);
}
Unity
You can call GenerateContentStreamAsync()
to stream generated text from text-only input.
using
Firebase
;
using
Firebase.AI
;
// Initialize the Gemini Developer API backend service
var
ai
=
FirebaseAI
.
GetInstance
(
FirebaseAI
.
Backend
.
GoogleAI
());
// Create a `GenerativeModel` instance with a model that supports your use case
var
model
=
ai
.
GetGenerativeModel
(
modelName
:
"gemini-3.5-flash"
);
// Provide a prompt that contains text
var
prompt
=
"Write a story about a magic backpack."
;
// To stream generated text output, call GenerateContentStreamAsync with the text input
var
responseStream
=
model
.
GenerateContentStreamAsync
(
prompt
);
await
foreach
(
var
response
in
responseStream
)
{
if
(
!
string
.
IsNullOrWhiteSpace
(
response
.
Text
))
{
UnityEngine
.
Debug
.
Log
(
response
.
Text
);
}
}
Learn how to choose a model
appropriate for your use case and app.
View example: Stream generated text from multimodal input
Swift
You can call generateContentStream()
to stream generated text from multimodal input of text and a single video.
import
FirebaseAILogic
// Initialize the Gemini Developer API backend service
let
ai
=
FirebaseAI
.
firebaseAI
(
backend
:
.
googleAI
())
// Create a `GenerativeModel` instance with a model that supports your use case
let
model
=
ai
.
generativeModel
(
modelName
:
"gemini-3.5-flash"
)
// Provide the video as `Data` with the appropriate MIME type
let
video
=
InlineDataPart
(
data
:
try
Data
(
contentsOf
:
videoURL
),
mimeType
:
"video/mp4"
)
// Provide a text prompt to include with the video
let
prompt
=
"What is in the video?"
// To stream generated text output, call generateContentStream with the text and video
let
contentStream
=
try
model
.
generateContentStream
(
video
,
prompt
)
for
try
await
chunk
in
contentStream
{
if
let
text
=
chunk
.
text
{
print
(
text
)
}
}
Kotlin
You can call generateContentStream()
to stream generated text from multimodal input of text and a single video.
For Kotlin, the methods in this SDK are suspend functions and need to be called
from a Coroutine scope
.
// Initialize the Gemini Developer API backend service
// Create a `GenerativeModel` instance with a model that supports your use case
val
model
=
Firebase
.
ai
(
backend
=
GenerativeBackend
.
googleAI
())
.
generativeModel
(
"gemini-3.5-flash"
)
val
contentResolver
=
applicationContext
.
contentResolver
contentResolver
.
openInputStream
(
videoUri
).
use
{
stream
->
stream
?.
let
{
val
bytes
=
stream
.
readBytes
()
// Provide a prompt that includes the video specified above and text
val
prompt
=
content
{
inlineData
(
bytes
,
"video/mp4"
)
text
(
"What is in the video?"
)
}
// To stream generated text output, call generateContentStream with the prompt
var
fullResponse
=
""
model
.
generateContentStream
(
prompt
).
collect
{
chunk
->
Log
.
d
(
TAG
,
chunk
.
text
?:
""
)
fullResponse
+=
chunk
.
text
}
}
}
Java
You can call generateContentStream()
to stream generated text from multimodal input of text and a single video.
For Java, the streaming methods in this SDK return a Publisher
type from the Reactive Streams library
.
// Initialize the Gemini Developer API backend service
// Create a `GenerativeModel` instance with a model that supports your use case
GenerativeModel
ai
=
FirebaseAI
.
getInstance
(
GenerativeBackend
.
googleAI
())
.
generativeModel
(
"gemini-3.5-flash"
);
// Use the GenerativeModelFutures Java compatibility layer which offers
// support for ListenableFuture and Publisher APIs
GenerativeModelFutures
model
=
GenerativeModelFutures
.
from
(
ai
);
ContentResolver
resolver
=
getApplicationContext
().
getContentResolver
();
try
(
InputStream
stream
=
resolver
.
openInputStream
(
videoUri
))
{
File
videoFile
=
new
File
(
new
URI
(
videoUri
.
toString
()));
int
videoSize
=
(
int
)
videoFile
.
length
();
byte
[]
videoBytes
=
new
byte
[
videoSize
]
;
if
(
stream
!=
null
)
{
stream
.
read
(
videoBytes
,
0
,
videoBytes
.
length
);
stream
.
close
();
// Provide a prompt that includes the video specified above and text
Content
prompt
=
new
Content
.
Builder
()
.
addInlineData
(
videoBytes
,
"video/mp4"
)
.
addText
(
"What is in the video?"
)
.
build
();
// To stream generated text output, call generateContentStream with the prompt
Publisher<GenerateContentResponse>
streamingResponse
=
model
.
generateContentStream
(
prompt
);
final
String
[]
fullResponse
=
{
""
};
streamingResponse
.
subscribe
(
new
Subscriber<GenerateContentResponse>
()
{
@Override
public
void
onNext
(
GenerateContentResponse
generateContentResponse
)
{
String
chunk
=
generateContentResponse
.
getText
();
fullResponse
[
0
]
+=
chunk
;
}
@Override
public
void
onComplete
()
{
System
.
out
.
println
(
fullResponse
[
0
]
);
}
@Override
public
void
onError
(
Throwable
t
)
{
t
.
printStackTrace
();
}
@Override
public
void
onSubscribe
(
Subscription
s
)
{
}
});
}
}
catch
(
IOException
e
)
{
e
.
printStackTrace
();
}
catch
(
URISyntaxException
e
)
{
e
.
printStackTrace
();
}
Web
You can call generateContentStream()
to stream generated text from multimodal input of text and a single video.
import
{
initializeApp
}
from
"firebase/app"
;
import
{
getAI
,
getGenerativeModel
,
GoogleAIBackend
}
from
"firebase/ai"
;
// TODO(developer) Replace the following with your app's Firebase configuration
// See: https://firebase.google.com/docs/web/learn-more#config-object
const
firebaseConfig
=
{
// ...
};
// Initialize FirebaseApp
const
firebaseApp
=
initializeApp
(
firebaseConfig
);
// Initialize the Gemini Developer API backend service
const
ai
=
getAI
(
firebaseApp
,
{
backend
:
new
GoogleAIBackend
()
});
// Create a `GenerativeModel` instance with a model that supports your use case
const
model
=
getGenerativeModel
(
ai
,
{
model
:
"gemini-3.5-flash"
});
// Converts a File object to a Part object.
async
function
fileToGenerativePart
(
file
)
{
const
base64EncodedDataPromise
=
new
Promise
((
resolve
)
=>
{
const
reader
=
new
FileReader
();
reader
.
onloadend
=
()
=>
resolve
(
reader
.
result
.
split
(
','
)[
1
]);
reader
.
readAsDataURL
(
file
);
});
return
{
inlineData
:
{
data
:
await
base64EncodedDataPromise
,
mimeType
:
file
.
type
},
};
}
async
function
run
()
{
// Provide a text prompt to include with the video
const
prompt
=
"What do you see?"
;
const
fileInputEl
=
document
.
querySelector
(
"input[type=file]"
);
const
videoPart
=
await
fileToGenerativePart
(
fileInputEl
.
files
[
0
]);
// To stream generated text output, call generateContentStream with the text and video
const
result
=
await
model
.
generateContentStream
([
prompt
,
videoPart
]);
for
await
(
const
chunk
of
result
.
stream
)
{
const
chunkText
=
chunk
.
text
();
console
.
log
(
chunkText
);
}
}
run
();
Dart
You can call generateContentStream()
to stream generated text from multimodal input of text and a single video.
import
'package:firebase_ai/firebase_ai.dart'
;
import
'package:firebase_core/firebase_core.dart'
;
import
'firebase_options.dart'
;
// Initialize FirebaseApp
await
Firebase
.
initializeApp
(
options:
DefaultFirebaseOptions
.
currentPlatform
,
);
// Initialize the Gemini Developer API backend service
// Create a `GenerativeModel` instance with a model that supports your use case
final
model
=
FirebaseAI
.
googleAI
().
generativeModel
(
model:
'gemini-3.5-flash'
);
// Provide a text prompt to include with the video
final
prompt
=
TextPart
(
"What's in the video?"
);
// Prepare video for input
final
video
=
await
File
(
'video0.mp4'
).
readAsBytes
();
// Provide the video as `Data` with the appropriate mimetype
final
videoPart
=
InlineDataPart
(
'video/mp4'
,
video
);
// To stream generated text output, call generateContentStream with the text and image
final
response
=
await
model
.
generateContentStream
([
Content
.
multi
([
prompt
,
videoPart
])
]);
await
for
(
final
chunk
in
response
)
{
print
(
chunk
.
text
);
}
Unity
You can call GenerateContentStreamAsync()
to stream generated text from multimodal input of text and a single video.
using
Firebase
;
using
Firebase.AI
;
// Initialize the Gemini Developer API backend service
var
ai
=
FirebaseAI
.
GetInstance
(
FirebaseAI
.
Backend
.
GoogleAI
());
// Create a `GenerativeModel` instance with a model that supports your use case
var
model
=
ai
.
GetGenerativeModel
(
modelName
:
"gemini-3.5-flash"
);
// Provide the video as `data` with the appropriate MIME type.
var
video
=
ModelContent
.
InlineData
(
"video/mp4"
,
System
.
IO
.
File
.
ReadAllBytes
(
System
.
IO
.
Path
.
Combine
(
UnityEngine
.
Application
.
streamingAssetsPath
,
"yourVideo.mp4"
)));
// Provide a text prompt to include with the video
var
prompt
=
ModelContent
.
Text
(
"What is in the video?"
);
// To stream generated text output, call GenerateContentStreamAsync with the text and video
var
responseStream
=
model
.
GenerateContentStreamAsync
(
new
[]
{
video
,
prompt
});
await
foreach
(
var
response
in
responseStream
)
{
if
(
!
string
.
IsNullOrWhiteSpace
(
response
.
Text
))
{
UnityEngine
.
Debug
.
Log
(
response
.
Text
);
}
}
Learn how to choose a model
appropriate for your use case and app.
Note that a file provided as inline data is encoded to base64 in transit, which
increases the size of the request. You get an HTTP 413 error if a request is
too large.
See Supported input files and requirements for the Vertex AI
Gemini API
to learn detailed information about the following:
Different options for providing a file in a request
(either inline or using the file's URL or URI)
Supported file types
Supported MIME types and how to specify them
Requirements and best practices for files and multimodal requests
What else can you do?
Learn how to count tokens
before sending long prompts to the model. Set up Cloud Storage for Firebase
so that you can include large files in your multimodal requests and have a
more managed solution for providing files in prompts.
Files can include images, PDFs, video, and audio.
Start thinking about preparing for production (see the production checklist
):
Try out other capabilities
Learn how to control content generation
You can also experiment with prompts and model configurations and even get a
generated code snippet using Google AI Studio
. Learn more about the supported models
Learn about the models available for various use cases
and their quotas
and pricing
.
Give feedback
about your experience with Firebase AI Logic
Send feedback
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License
, and code samples are licensed under the Apache 2.0 License
. For details, see the Google Developers Site Policies
. Java is a registered trademark of Oracle and/or its affiliates.
Last updated 2026-05-27 UTC.
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